1. Field of Invention
This invention relates generally to Automated Material Handling Systems (AMHS) and, more particularly, to control systems that predict accurate transmission and storage paths for transportation of material in an manufacturing Integrated Circuit (IC) manufacturing foundry.
2. Description of Related Art
A typical IC manufacturing foundry, or Fab, has a control system, AMHS, that provides for the transmission of the product wafers from one manufacturing point to another. Accurate transmission positively affects the time in which lots are processed and the overall wafer lot processing cycle time. These AMHS control systems run complex manufacturing lines. Constant changes in product and various process flows is can create problems. A change in manufacturing strategy because of market conditions is an added complication that can add to the problems.
AMHS tools help control work lot transmission and storage during processing in a Fab. Typically, there are no means in place to predict an optimum tool destination the lot should be at for best cycle time through the Fab. When tool destinations are not accurate, another transmission must be preformed manually to take the lot to the correct station, or stocker, for it""s next processing. A manufacturing person must physically move the lot from the incorrect stocking area to the correct one. This results in a significant loss of time and the reduction of productivity for the entire Fab line. Furthermore, with a work lot at an incorrect destination there is the probability of the correct tool being idle while that particular lot is unavailable.
Several methods or systems related to manufacturing scheduling and process control are available. In U.S. Pat. No. 5,432,887 (Khaw) a neural network system and method for factory scheduling is described. In U.S. Pat. No. 5,619,695 (Arbabi et al.) a method and apparatus for scheduling resources is described. In U.S. Pat. No. 5,845,258 (Kennedy) a strategy driven planning system is provided. In U.S. Pat. No. 6,055,533 (Hogge) a software system utilizing a filtering priority queue is described. Finally, in U.S. Pat. No. 5,311,421 (Nomura et al.) a process control method by use of a neural network is described.
An AMHS with optimum destination prediction control (which would place work lots in there right place at the right time) is needed to reduce both work lot cycle time and costs due to idle tools.
This invention""s overall objective is to provide a flow control process that can predict their optimum transmission path for work lots to their next work position. This significantly reduces the turn around time for processing in a complex manufacturing process such as a wafer Fab line.
A second more specific objective is to provide a Structured Queuing Language (SQL) algorithm that can determine the key information to retrieve for generation of optimized transmission and storage instructions for manufacturing. Additionally, this SQL algorithm sorts history records retrieved from the AMHS so that are they can be more easily used to optimize the prediction control process.
An additional objective is to retrieve the history records on a continuous basis in order to quickly adapt the system according to recent past behavior. This can be thought of as a learning stage for the new method. The retrieval of past records is key to this prediction method and is used continuously along the Fab line to increase accuracy.
An additional objective is to collect the time stamps, location, stage, too capability, and next required tool capability of each lot porcessed in order to adapt the current Fab activity. This is another method of improving the prediction as the Fab line continues operation.
Another objective is to run a stored programming procedure that analyzes current activity on the Fab and from that develops the lot distribution table of successful hit ratios for manufacturing transmissions. This table is the routing of the optimum paths for the work lots to take between tool stockers and contains the calculated hit ratios of successful moves. It also takes into account current activity so that incorrect destinations and thus loss of time are greatly reduced.
These objectives are achieved by the method of this invention. Through the analysis of past and current data contained in the AMHS, the running stored procedure is able in real time to provide accurate predictions of the proper movement of work lots. This eliminates the need for manual intervention to move the work lots to the correct tool destination.